Statistical models for quantifying diagnostic accuracy with multiple lesions per patient.

Biostatistics
Aeilko H ZwindermanJaap Stoker

Abstract

We propose random-effects models to summarize and quantify the accuracy of the diagnosis of multiple lesions on a single image without assuming independence between lesions. The number of false-positive lesions was assumed to be distributed as a Poisson mixture, and the proportion of true-positive lesions was assumed to be distributed as a binomial mixture. We considered univariate and bivariate, both parametric and nonparametric mixture models. We applied our tools to simulated data and data of a study assessing diagnostic accuracy of virtual colonography with computed tomography in 200 patients suspected of having one or more polyps.

References

Feb 12, 2002·Statistics in Medicine·Hans C van HouwelingenTheo Stijnen
Apr 30, 2004·Statistics in Medicine·Peter MartusAndreas Ziegler

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Citations

May 3, 2013·Medical Physics·Andriy I BandosDavid Gur
Sep 29, 2012·The Breast Journal·Chaya S MoskowitzMaxine Jochelson
Sep 8, 2009·Medical Decision Making : an International Journal of the Society for Medical Decision Making·Thomas A TrikalinosJoseph Lau
Sep 27, 2016·Asia-Pacific Journal of Clinical Oncology·Vikram JoshiUNKNOWN Elements of Cancer Care (EoCC) Investigators
Jan 25, 2018·JAMA : the Journal of the American Medical Association·Matthew D F McInnesBrian H Willis
Oct 7, 2021·The Cochrane Database of Systematic Reviews·Tin NadarevicDavor Štimac

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